SMARCE1 deficiency generates a targetable mSWI/SNF dependency in clear cell meningioma.

Mammalian SWI/SNF (mSWI/SNF) ATP-dependent chromatin remodeling complexes establish and maintain chromatin accessibility and gene expression, and are frequently perturbed in cancer. Clear cell meningioma (CCM), an aggressive tumor of the central nervous system, is uniformly driven by loss of SMARCE1, an integral subunit of the mSWI/SNF core. Here, we identify a structural role for SMARCE1 in selectively stabilizing the canonical BAF (cBAF) complex core-ATPase module interaction. In CCM, cBAF complexes fail to stabilize on chromatin, reducing enhancer accessibility, and residual core module components increase the formation of BRD9-containing non-canonical BAF (ncBAF) complexes. Combined attenuation of cBAF function and increased ncBAF complex activity generates the CCM-specific gene expression signature, which is distinct from that of NF2-mutated meningiomas. Importantly, SMARCE1-deficient cells exhibit heightened sensitivity to small-molecule inhibition of ncBAF complexes. These data inform the function of a previously elusive SWI/SNF subunit and suggest potential therapeutic approaches for intractable SMARCE1-deficient CCM tumors.

SMARCB1 is required for widespread BAF complex-mediated activation of enhancers and bivalent promoters.

Perturbations to mammalian SWI/SNF (mSWI/SNF or BAF) complexes contribute to more than 20% of human cancers, with driving roles first identified in malignant rhabdoid tumor, an aggressive pediatric cancer characterized by biallelic inactivation of the core BAF complex subunit SMARCB1 (BAF47). However, the mechanism by which this alteration contributes to tumorigenesis remains poorly understood. We find that BAF47 loss destabilizes BAF complexes on chromatin, absent significant changes in complex assembly or integrity. Rescue of BAF47 in BAF47-deficient sarcoma cell lines results in increased genome-wide BAF complex occupancy, facilitating widespread enhancer activation and opposition of Polycomb-mediated repression at bivalent promoters. We demonstrate differential regulation by two distinct mSWI/SNF assemblies, BAF and PBAF complexes, enhancers and promoters, respectively, suggesting that each complex has distinct functions that are perturbed upon BAF47 loss. Our results demonstrate collaborative mechanisms of mSWI/SNF-mediated gene activation, identifying functions that are co-opted or abated to drive human cancers and developmental disorders.

Prediction of potent shRNAs with a sequential classification algorithm.

We present SplashRNA, a sequential classifier to predict potent microRNA-based short hairpin RNAs (shRNAs). Trained on published and novel data sets, SplashRNA outperforms previous algorithms and reliably predicts the most efficient shRNAs for a given gene. Combined with an optimized miR-E backbone, >90% of high-scoring SplashRNA predictions trigger >85% protein knockdown when expressed from a single genomic integration. SplashRNA can significantly improve the accuracy of loss-of-function genetics studies and facilitates the generation of compact shRNA libraries.

Linear mixed model for heritability estimation that explicitly addresses environmental variation.

The linear mixed model (LMM) is now routinely used to estimate heritability. Unfortunately, as we demonstrate, LMM estimates of heritability can be inflated when using a standard model. To help reduce this inflation, we used a more general LMM with two random effects-one based on genomic variants and one based on easily measured spatial location as a proxy for environmental effects. We investigated this approach with simulated data and with data from a Uganda cohort of 4,778 individuals for 34 phenotypes including anthropometric indices, blood factors, glycemic control, blood pressure, lipid tests, and liver function tests. For the genomic random effect, we used identity-by-descent estimates from accurately phased genome-wide data. For the environmental random effect, we constructed a covariance matrix based on a Gaussian radial basis function. Across the simulated and Ugandan data, narrow-sense heritability estimates were lower using the more general model. Thus, our approach addresses, in part, the issue of "missing heritability" in the sense that much of the heritability previously thought to be missing was fictional. Software is available at https://github.com/MicrosoftGenomics/FaST-LMM.

Further improvements to linear mixed models for genome-wide association studies.

We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science.

Design and validation of a program to identify inadequate intake of iron, zinc, and vitamin A.

OBJECTIVE: The aim of this study was to create an innovative, easy-to-use, and informative web-based application system to calculate the iron, zinc, and vitamin A intake in comparison to the recommended nutrient intake (RNI). METHODS: Food survey data on commonly consumed Indonesian foods (122 foods) were recorded from 68,800 households. The data were used to extract 13 relevant food groups representing typical Indonesian dietary patterns and were incorporated into the development of new software called the Calculator of Inadequate Micronutrient Intake (CIMI). To validate, 24-h dietary recall data of children (n = 118) and adult women (n = 124) from Indonesia were collected and analyzed using CIMI and Nutrisurvey (NS) and the results were analyzed. RESULTS: CIMI calculates the absolute intake of energy, macronutrients, retinol, ß-carotene, retinol equivalents, iron, and zinc. The percentages of RNI fulfillment with regard to age and sex are presented. The bioavailability levels of iron and zinc are considered. Validation showed that results of CIMI and NS were comparable with regard to the average intake and range of data distribution ratio. Due to bioavailability factors, more participants who had intake of iron (adult group) and zinc (both groups) below the cutoff were detected by CIMI. The range of correlation coefficient of NS and CIMI were from 0.889 (energy) to 0.713 (iron) in the children's group and from 0.919 (protein) to 0.686 (vitamin A) in the adult group. CONCLUSION: CIMI is a simple and rapid tool that calculates energy and nutrient intake, and also the percentage of nutrient fulfillment in comparison with the dietary recommendation.

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants.

MOTIVATION: Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test-a score test-with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene-gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus we develop new computationally efficient methods. RESULTS: After reviewing theoretical differences in performance between the score and LR tests, we find empirically on real data that the LR test generally has more power. In particular, on 15 of 17 real datasets, the LR test yielded at least as many associations as the score test-up to 23 more associations-whereas the score test yielded at most one more association than the LR test in the two remaining datasets. On synthetic data, we find that the LR test yielded up to 12% more associations, consistent with our results on real data, but also observe a regime of extremely small signal where the score test yielded up to 25% more associations than the LR test, consistent with theory. Finally, our computational speedups now enable (i) efficient LR testing when the background kernel is full rank, and (ii) efficient score testing when the background kernel changes with each test, as for gene-gene interaction tests. The latter yielded a factor of 2000 speedup on a cohort of size 13 500. AVAILABILITY: Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/. CONTACT: heckerma@microsoft.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images.

Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cellular nuclear segmentation in highly anisotropic and noisy 3D image data. Manually localizing and segmenting each and every cellular nucleus is very time-consuming, which remains a bottleneck in large-scale biological experiments. In this work, we present a tool for automated segmentation of cellular nuclei from 3D fluorescent microscopic data. Our tool is based on state-of-the-art image processing and machine learning techniques and provides a user-friendly graphical user interface. We show that our tool is as accurate as manual annotation and greatly reduces the time for the registration.

Determinants of robustness in spindle assembly checkpoint signalling.

The spindle assembly checkpoint is a conserved signalling pathway that protects genome integrity. Given its central importance, this checkpoint should withstand stochastic fluctuations and environmental perturbations, but the extent of and mechanisms underlying its robustness remain unknown. We probed spindle assembly checkpoint signalling by modulating checkpoint protein abundance and nutrient conditions in fission yeast. For core checkpoint proteins, a mere 20% reduction can suffice to impair signalling, revealing a surprising fragility. Quantification of protein abundance in single cells showed little variability (noise) of critical proteins, explaining why the checkpoint normally functions reliably. Checkpoint-mediated stoichiometric inhibition of the anaphase activator Cdc20 (Slp1 in Schizosaccharomyces pombe) can account for the tolerance towards small fluctuations in protein abundance and explains our observation that some perturbations lead to non-genetic variation in the checkpoint response. Our work highlights low gene expression noise as an important determinant of reliable checkpoint signalling.

A spatial and temporal map of C. elegans gene expression.

The C. elegans genome has been completely sequenced, and the developmental anatomy of this model organism is described at single-cell resolution. Here we utilize strategies that exploit this precisely defined architecture to link gene expression to cell type. We obtained RNAs from specific cells and from each developmental stage using tissue-specific promoters to mark cells for isolation by FACS or for mRNA extraction by the mRNA-tagging method. We then generated gene expression profiles of more than 30 different cells and developmental stages using tiling arrays. Machine-learning-based analysis detected transcripts corresponding to established gene models and revealed novel transcriptionally active regions (TARs) in noncoding domains that comprise at least 10% of the total C. elegans genome. Our results show that about 75% of transcripts with detectable expression are differentially expressed among developmental stages and across cell types. Examination of known tissue- and cell-specific transcripts validates these data sets and suggests that newly identified TARs may exercise cell-specific functions. Additionally, we used self-organizing maps to define groups of coregulated transcripts and applied regulatory element analysis to identify known transcription factor- and miRNA-binding sites, as well as novel motifs that likely function to control subsets of these genes. By using cell-specific, whole-genome profiling strategies, we have detected a large number of novel transcripts and produced high-resolution gene expression maps that provide a basis for establishing the roles of individual genes in cellular differentiation.

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning.

BACKGROUND: The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published q-Norm MKL algorithm. RESULTS: We demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities ab initio along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against. CONCLUSIONS: We present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.

Exploiting physico-chemical properties in string kernels.

BACKGROUND: String kernels are commonly used for the classification of biological sequences, nucleotide as well as amino acid sequences. Although string kernels are already very powerful, when it comes to amino acids they have a major short coming. They ignore an important piece of information when comparing amino acids: the physico-chemical properties such as size, hydrophobicity, or charge. This information is very valuable, especially when training data is less abundant. There have been only very few approaches so far that aim at combining these two ideas. RESULTS: We propose new string kernels that combine the benefits of physico-chemical descriptors for amino acids with the ones of string kernels. The benefits of the proposed kernels are assessed on two problems: MHC-peptide binding classification using position specific kernels and protein classification based on the substring spectrum of the sequences. Our experiments demonstrate that the incorporation of amino acid properties in string kernels yields improved performances compared to standard string kernels and to previously proposed non-substring kernels. CONCLUSIONS: In summary, the proposed modifications, in particular the combination with the RBF substring kernel, consistently yield improvements without affecting the computational complexity. The proposed kernels therefore appear to be the kernels of choice for any protein sequence-based inference. AVAILABILITY: Data sets, code and additional information are available from http://www.fml.tuebingen.mpg.de/raetsch/suppl/aask. Implementations of the developed kernels are available as part of the Shogun toolbox.

Stress-induced changes in the Arabidopsis thaliana transcriptome analyzed using whole-genome tiling arrays.

The responses of plants to abiotic stresses are accompanied by massive changes in transcriptome composition. To provide a comprehensive view of stress-induced changes in the Arabidopsis thaliana transcriptome, we have used whole-genome tiling arrays to analyze the effects of salt, osmotic, cold and heat stress as well as application of the hormone abscisic acid (ABA), an important mediator of stress responses. Among annotated genes in the reference strain Columbia we have found many stress-responsive genes, including several transcription factor genes as well as pseudogenes and transposons that have been missed in previous analyses with standard expression arrays. In addition, we report hundreds of newly identified, stress-induced transcribed regions. These often overlap with known, annotated genes. The results are accessible through the Arabidopsis thaliana Tiling Array Express (At-TAX) homepage, which provides convenient tools for displaying expression values of annotated genes, as well as visualization of unannotated transcribed regions along each chromosome.

At-TAX: a whole genome tiling array resource for developmental expression analysis and transcript identification in Arabidopsis thaliana.

Gene expression maps for model organisms, including Arabidopsis thaliana, have typically been created using gene-centric expression arrays. Here, we describe a comprehensive expression atlas, Arabidopsis thaliana Tiling Array Express (At-TAX), which is based on whole-genome tiling arrays. We demonstrate that tiling arrays are accurate tools for gene expression analysis and identified more than 1,000 unannotated transcribed regions. Visualizations of gene expression estimates, transcribed regions, and tiling probe measurements are accessible online at the At-TAX homepage.